Pitcher Precision

Sasank Vishnubhatla

4/17/2019

Last Update: 2019-04-24 10:15:42

Libraries

Let’s load some libraries in first.

library(baseballr)
library(pitchRx)
library(tidyverse)

Let’s also clean out environment.

rm(list = ls())

With these libraries, we can get out data as well as visaulize it. Let’s take a look at some players to see what we can look at.

Data Loading

Here are the list of players I will be looking at.

Let’s now scrape the data for each player.

scrape.data = function(start, id) {
    data = scrape_statcast_savant(start_date = start,
                                  end_date = "2019-04-22",
                                  playerid = id,
                                  player_type = 'pitcher')
    data
}

start = "2019-01-01"

syndergaard.data = scrape.data(start, 592789)
corbin.data = scrape.data(start, 571578)
vazquez.data = scrape.data(start, 553878)
stroman.data = scrape.data(start, 573186)
verlander.data = scrape.data(start, 434378)
treinen.data = scrape.data(start, 595014)

Now with our data, let’s get the information we want out of it.

filter.data = function(data) {
    filtered = data.frame(name = data %>% pull(player_name),
                          pitch = data %>% pull(pitch_type),
                          outcome = data %>% pull(type),
                          date = data %>% pull(game_date),
                          event = data %>% pull(events),
                          descrip = data %>% pull(description),
                          xcoord = data %>% pull(plate_x),
                          ycoord = data %>% pull(plate_z),
                          xmove = data %>% pull(pfx_x),
                          ymove = data %>% pull(pfx_z),
                          velo = data %>% pull(effective_speed),
                          spin = data %>% pull(release_spin_rate),
                          exvelo = data %>% pull(launch_speed),
                          exang = data %>% pull(launch_angle))
    filtered
}

syndergaard = filter.data(syndergaard.data)
corbin = filter.data(corbin.data)
stroman = filter.data(stroman.data)
treinen = filter.data(treinen.data)
vazquez = filter.data(vazquez.data)
verlander = filter.data(verlander.data)

With this filtered data, we have selected the following columns:

Visualization

Let’s start visualizing some of this data. Before that, let me define a strikezone. This strikezone was taken from the website Baseball with R

topKzone = 3.5
botKzone = 1.6
inKzone = -.95
outKzone = 0.95
kZone = data.frame(x = c(inKzone, inKzone, outKzone, outKzone, inKzone),
                   y = c(botKzone, topKzone, topKzone, botKzone, botKzone))

Pitch Location via Pitch Type

Let’s look at pitch location via pitch type.

graph.pitch.heatmap.type = function(player.data) {
    graph = ggplot(player.data) +
        geom_jitter(aes(x = player.data$xcoord,
                        y = player.data$ycoord,
                        color = player.data$pitch)) +
        xlab("Horizontal Position") +
        ylab("Vertical Position") +
        ggtitle(paste(player.data$name[1], "Heatmap", sep = " ")) +
        labs(color = "Pitch Type") +
        theme_minimal() + geom_path(aes(x, y), data = kZone)
    graph
}

corbin.heatmap.type = graph.pitch.heatmap.type(corbin)
corbin.heatmap.type

stroman.heatmap.type = graph.pitch.heatmap.type(stroman)
stroman.heatmap.type

syndergaard.heatmap.type = graph.pitch.heatmap.type(syndergaard)
syndergaard.heatmap.type

treinen.heatmap.type = graph.pitch.heatmap.type(treinen)
treinen.heatmap.type

vazquez.heatmap.type = graph.pitch.heatmap.type(vazquez)
vazquez.heatmap.type

verlander.heatmap.type = graph.pitch.heatmap.type(verlander)
verlander.heatmap.type

Pitch Location via Velocity

Let’s look at pitch location via velocity.

graph.pitch.heatmap.velo = function(player.data) {
    graph = ggplot(player.data) +
        geom_jitter(aes(x = player.data$xcoord,
                        y = player.data$ycoord,
                        color = player.data$velo)) +
        xlab("Horizontal Position") +
        ylab("Vertical Position") +
        ggtitle(paste(player.data$name[1], "Heatmap", sep = " ")) +
        labs(color = "Velocity") +
        scale_color_gradient(low = "blue", high = "red") +
        theme_minimal() + geom_path(aes(x, y), data = kZone)
    graph
}

corbin.heatmap.velo = graph.pitch.heatmap.velo(corbin)
corbin.heatmap.velo

stroman.heatmap.velo = graph.pitch.heatmap.velo(stroman)
stroman.heatmap.velo

syndergaard.heatmap.velo = graph.pitch.heatmap.velo(syndergaard)
syndergaard.heatmap.velo

treinen.heatmap.velo = graph.pitch.heatmap.velo(treinen)
treinen.heatmap.velo

vazquez.heatmap.velo = graph.pitch.heatmap.velo(vazquez)
vazquez.heatmap.velo

verlander.heatmap.velo = graph.pitch.heatmap.velo(verlander)
verlander.heatmap.velo

Pitch Movement

Pitch Velocity

We need to separate each pitch first by type. Then we can see how the pitch’s velocity changed over time.

graph.pitch.velo = function(player) {
    # player = player[complete.cases(player),]
    graph = ggplot(player) +
        geom_line(aes(x = 1:length(player$velo),
                      y = player$velo,
                      color = player$pitch)) +
        xlab("Pitches Thrown") + ylab("Velocity") + labs(color = "Pitch Type") +
        ggtitle(paste(player$name[1], "Pitch Velocity Chart", sep = " ")) +
        theme_minimal()
}

corbin.velo = graph.pitch.velo(corbin)
corbin.velo

stroman.velo = graph.pitch.velo(stroman)
stroman.velo

syndergaard.velo = graph.pitch.velo(syndergaard)
syndergaard.velo

treinen.velo = graph.pitch.velo(treinen)
treinen.velo

vazquez.velo = graph.pitch.velo(vazquez)
vazquez.velo

verlander.velo = graph.pitch.velo(verlander)
verlander.velo

Pitch Spin Rate